Topic outline

  • Welcome to Data Mining

    Teacher Information:

    Fariha Jahan
    Lecturer, Department of Computer Science & Engineering
    Daffodil International University(DIU)
    Dhaka, Bangladesh
    Contact: +8801793819157
    Web Profile: Fariha Jahan



    Course Rationale
         An introduction to data mining; Data preparation, model building, and data mining techniques such as clustering, decisions trees and neural networks; Induction of predictive models from data: classification, regression, and probability estimation; Application case studies; Data-mining software tools review and comparison.

    Course Objectives

    • To apply the concept of data mining in solving problems
    • To demonstrate applications of data mining using tools
    • To apply knowledge of data mining in project work

    Course Outcomes (CO’s)

    • CO1 Able to possess the basic knowledge of Weka and Python concerning data mining and machine learning
    • CO2 Able to implement different data mining and machine learning algorithms like classification, prediction, clustering and association rule mining to solve real-world problems using Weka and Python
    • CO3 Able to compare and evaluate different data mining and machine learning algorithms like classification, prediction, clustering and association rule mining using Weka and/or Python
    • CO4 Able to apply implementation knowledge of data mining and machine learning in developing research ideas
    Grading Scheme
    Attendance: 10%
    Lab Performance: 25%
    Project / Lab Report: 25%
    Final Exam: 40%

    • Recommended Books
    1. Introduction to Data Mining and Applications
    2. Data Mining Concepts and Techniques
    3. Data Mining Techniques
    4. Data Mining Using Weka
    5. Weka Manual
    6. Data Mining Using Python
    • Global Data Repository for Data Mining and/or Machine Learning
    1. WISDM
    2. UCI ML Repository
    3. KDD Cup
    4. Kaggle
    5. KDnuggets

    • Standard Templates
    1. IEEE Template
    2. ACM Template

  • Week 1: Introduction to Weka

    Topics of Discussion

    • Introduction to Weka
    • Relationship to data mining
    • Overview of data mining with Weka


    Expected Learning Outcome

    • Appreciation of the needs of data mining with Weka
    • Visualization of the relationship of Weka to data mining
    • Visualization of different data mining tasks with Weka

  • Week 2: Data Visualization Using Weka

    Topics of Discussion

    • Review of data mining task and related application examples
    • Data Visualization with Weka
    • Course Project Team and discussion


    Expected Learning Outcome

    • On-hand acquaintance and practice of data visualization with Weka
    • Team formation for the course project


  • Week 3: Feature/Attribute Selection Using Weka

    Topics of Discussion

    • Discussion on feature/attribute selection 
    • Project Discussion and execution plan

    Expected Learning Outcome

    • On-hand acquaintance and practice of feature/attribute selection with Weka
    • Selection of project topic by team


    Week 3 Class Recording

  • Week 4: Classification Using Weka

    Topics of Discussion

    • Classification and prediction with Weka
    • Classification: decision tree

    Expected Learning Outcome

    • Problem solving skill in classification and prediction
    • Skill in using Weka as a data mining tool for classification and prediction


    Week 4 Class Recording

  • Week 5: Continuation of Classification Using Weka

    Topics of Discussion

    • Classification and prediction with Weka
    • Classification: Bayesian, Instance-based

    Expected Learning Outcome

    • Problem solving skill in classification and prediction
    • Skill in using Weka as a data mining tool for classification and prediction


    Week 5 class recording[Part 1]

    Week 5 class recording[Part 2]


  • Week 6: Cluster Analysis Using Weka

    Topics of Discussion

    • Cluster Analysis with Weka
    • Cluster Analysis: partitional (K-means), hierarchical, density-based


    Expected Learning Outcome

    • Problem solving skill in classification and prediction
    • Skill in using Weka as a data mining tool for cluster analysis


    Week 6 Class recording

  • Week 7: Mid Exam

    Midterm Examination Week




    • Week 8: Presentation of Project # 1 (Using Weka)

      Project # 1 (with Weka) Presentation



      • Week 9: Introduction to Python

        Topics of Discussion

        • Introduction to Python
        • Relationship to machine learning
        • Overview of machine learning with Python

        Expected Learning Outcome

        • Appreciation of the needs of machine learning with Python
        • Visualization of the relationship of Python to machine learning
        • Visualization of different machine learning tasks with Python


        Week 9 Class Recording[Part 1]

        Week 9 Class Recording[Part 2]

      • Week 10: Classification Using Python


        Topics of Discussion

        • Classification and prediction with Python
        • Classification: decision tree

        Expected Learning Outcome

        • Problem solving skill in classification and prediction
        • Skill in using Weka as a data mining tool for classification and prediction


        Week 10 Class Recording

      • Week 11: Classification Using Python (Continued)


        Topics of Discussion

        • Classification and prediction with Python
        • Classification: decision tree


        Expected Learning Outcome

        • Problem solving skill in classification and prediction
        • Skill in using Weka as a data mining tool for classification and prediction

      • Week 12: Cluster Analysis Using Python


        Topics of Discussion

        • Cluster Analysis with Python
        • Cluster Analysis: partitional (K-means), hierarchical, density-based


        Expected Learning Outcome

        • Problem solving skill in classification and prediction
        • Skill in using Weka as a data mining tool for cluster analysis
        • Ability to apply data mining knowledge in development project

      • Week 13: Presentation of Project # 1 (Using Python)

        Project # 2 (with Python) Presentation




        • Week 14: Final Examination

          Semester Final Examination Week


          Topics to be included in final exam:

          • Classification (with Weka and Python)
          • Cluster Analysis (with Weka and Python)

        • Topic 15